#!pip install pmdarima
#!pip install fastparquet
#!pip install keras_tuner
#!pip install tensorflow

import math
import numpy as np
import pandas as pd
from tqdm import tqdm
import matplotlib.pyplot as plt
from pmdarima import auto_arima
from keras import Input
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.metrics import mean_absolute_percentage_error
from utils.bigquery_handler import BigQueryHandler

pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)

bqh = BigQueryHandler('unir-predictiv0-andina-espana')

query = f"""
    SELECT 
        *
    FROM `unir-predictiv0-andina-espana.datacomex.comex_comunidad_andina_modelo` 
    LIMIT 5
"""

data = bqh.get_bigquery_data(query)

data.head()

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 18 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   fecha                5 non-null      dbdate 
 1   anio                 5 non-null      Int64  
 2   mes                  5 non-null      Int64  
 3   tipo_movimiento_cod  5 non-null      object 
 4   tipo_movimiento      5 non-null      object 
 5   estado_transaccion   5 non-null      object 
 6   cod_pais             5 non-null      object 
 7   nombre_pais          5 non-null      object 
 8   cod_provincia        5 non-null      object 
 9   nombre_provincia     5 non-null      object 
 10  cod_comunidad        5 non-null      object 
 11  nombre_comunidad     5 non-null      object 
 12  nivel_taric          5 non-null      object 
 13  cod_taric            5 non-null      object 
 14  descripcion_taric    5 non-null      object 
 15  euros                5 non-null      float64
 16  dolares              5 non-null      float64
 17  kilogramos           5 non-null      float64
dtypes: Int64(2), dbdate(1), float64(3), object(12)
memory usage: 862.0+ bytes

data['fecha'] = pd.to_datetime(data['fecha'])
print(data.dtypes)

fecha                  datetime64[ns]
anio                            Int64
mes                             Int64
tipo_movimiento_cod            object
tipo_movimiento                object
estado_transaccion             object
cod_pais                       object
nombre_pais                    object
cod_provincia                  object
nombre_provincia               object
cod_comunidad                  object
nombre_comunidad               object
nivel_taric                    object
cod_taric                      object
descripcion_taric              object
euros                         float64
dolares                       float64
kilogramos                    float64
dtype: object

def get_taric_data():
    query = f"""
        SELECT
            fecha,
            cod_taric,
            descripcion_taric,
            sum(dolares) as dolares,
            sum(kilogramos) as kilogramos
        FROM `unir-predictiv0-andina-espana.datacomex.comex_comunidad_andina_modelo`
        WHERE nivel_taric = '1'
        GROUP BY 1, 2, 3
    """
    data = bqh.get_bigquery_data(query)
    data['fecha'] = pd.to_datetime(data['fecha'])
    return data

data = get_taric_data()
data.head()

if data['fecha'].isna().any():
    print("La columna 'Fecha' contiene valores nulos.")
else:
    print("La columna 'Fecha' no tiene valores nulos.")

La columna 'Fecha' no tiene valores nulos.

def plot_time_series(df, num_columns=3, y_label="Y Label", x_label="Fecha"):
    num_plots = len(df.columns)
    num_rows = math.ceil(num_plots / num_columns)
    fig, axes = plt.subplots(
        num_rows, num_columns, figsize=(15, num_rows * 4), constrained_layout=True
    )
    axes = axes.flatten()

    for i, col in enumerate(df.columns):
        axes[i].plot(df.index, df[col], label=col)
        axes[i].set_title(f"Serie de tiempo: {col}")
        axes[i].set_xlabel(x_label)
        axes[i].set_ylabel(y_label)
        axes[i].legend()

    for i in range(num_plots, len(axes)):
        fig.delaxes(axes[i])

    plt.show()

def fillna(df, type = 'number'):
    numeric_columns = df.select_dtypes(include=[type]).columns
    df[numeric_columns] = df[numeric_columns].fillna(0)
    return df

def generate_target_values_taric(df, agrupation_column, target_column,  fillna_flag = False):
    df = (
        df.groupby(["fecha", agrupation_column])
        .agg(**{f"{target_column}_sum": (target_column, "sum")})
        .reset_index()
    )

    df = df.pivot(
        index="fecha", columns=agrupation_column, values=[f"{target_column}_sum"]
    )

    df.columns = [
        f"{target_column}_producto_" + str(col[1]).strip() for col in df.columns.values
    ]

    df.reset_index(inplace=True)
    return fillna(df) if fillna_flag else df

df = generate_target_values_taric(data, "descripcion_taric", "kilogramos", fillna_flag= False)
df.head(3)

null_percentage = (df.isna().sum() / df.shape[0]) * 100
print(null_percentage)

fecha                                                         0.000000
kilogramos_producto_Abonos                                    0.564972
kilogramos_producto_Aceites esenciales; perfumer              0.000000
kilogramos_producto_Aeronaves; vehículos espaciales           2.542373
kilogramos_producto_Alfombras; revesti textiles suelo         9.039548
kilogramos_producto_Algodón                                   0.000000
kilogramos_producto_Aluminio y sus manufacturas               0.282486
kilogramos_producto_Animales vivos                           11.299435
kilogramos_producto_Aparatos y material eléctricos            0.000000
kilogramos_producto_Aparatos ópticos, medida, médicos         0.000000
kilogramos_producto_Armas y municiones; accesorios            4.237288
kilogramos_producto_Artíc textilhogar; prendería              0.282486
kilogramos_producto_Artículos de sombrerería                  0.847458
kilogramos_producto_Azúcares; artículos confitería            0.000000
kilogramos_producto_Barcos y embarcaciones                   16.101695
kilogramos_producto_Bebidas todo tipo (exc zumos)             0.000000
kilogramos_producto_Cacao y sus preparaciones                 0.000000
kilogramos_producto_Café, té, yerba mate y especias           0.000000
kilogramos_producto_Calzado; sus partes                       0.000000
kilogramos_producto_Carne y despojos comestibles              1.977401
kilogramos_producto_Caucho y sus manufacturas                 0.000000
kilogramos_producto_Cereales                                  0.282486
kilogramos_producto_Cinc y sus manufacturas                  16.101695
kilogramos_producto_Cobre y sus manufacturas                  1.977401
kilogramos_producto_Combustibles, aceites mineral             0.000000
kilogramos_producto_Conjunt de otros productos                0.000000
kilogramos_producto_Conjunt indust; regl 840/96              92.937853
kilogramos_producto_Conservas de carne o pescado              0.000000
kilogramos_producto_Conservas verdura o fruta; zumo           0.000000
kilogramos_producto_Corcho y sus manufacturas                25.423729
kilogramos_producto_Estaño y sus manufacturas                 0.282486
kilogramos_producto_Fibras sinté, artif discont               0.000000
kilogramos_producto_Fieltro, tela s/ tejer; cordelería        1.412429
kilogramos_producto_Filamentos sintéticos o artificiales      0.000000
kilogramos_producto_Frutas /frutos, s/ conservar              0.000000
kilogramos_producto_Fundición, hierro y acero                 0.000000
kilogramos_producto_Grasas, aceite animal o vegetal           0.282486
kilogramos_producto_Herramien y cuchill metálic               0.000000
kilogramos_producto_Instrumentos musicales                    1.129944
kilogramos_producto_Jabones; lubricantes; ceras               0.000000
kilogramos_producto_Jugos y extractos vegetales               2.824859
kilogramos_producto_Juguetes, juegos, artíc deportivos        0.000000
kilogramos_producto_Lana y pelo fino                          1.129944
kilogramos_producto_Leche, productos lácteos; huevos          1.129944
kilogramos_producto_Legumbres, hortalizas, s/ conserv         0.000000
kilogramos_producto_Libros, publicaci; artes gráf             0.000000
kilogramos_producto_Madera y sus manufacturas                 0.000000
kilogramos_producto_Manuf de fundic, hier/acero               0.000000
kilogramos_producto_Manufact de cuero, marroquinería          0.000000
kilogramos_producto_Manufact diversas de metales              0.000000
kilogramos_producto_Manufacturas de cestería                 11.864407
kilogramos_producto_Manufacturas de piedra, yeso              0.000000
kilogramos_producto_Manufacturas diversas                     0.000000
kilogramos_producto_Mat plásticas; sus manufactu              0.000000
kilogramos_producto_Materias albuminóideas; colas             0.000000
kilogramos_producto_Materias trenzables                      14.124294
kilogramos_producto_Minerales, escorias y cenizas             1.977401
kilogramos_producto_Muebles, sillas, lámparas                 0.000000
kilogramos_producto_Máquinas y aparatos mecánicos             0.000000
kilogramos_producto_Níquel y sus manufacturas                72.033898
kilogramos_producto_Objetos de arte; antiguedades             2.259887
kilogramos_producto_Otras fibras textiles vegetales           3.107345
kilogramos_producto_Otros metal comunes; cermets             24.576271
kilogramos_producto_Otros productos de origen animal          3.107345
kilogramos_producto_Otros productos químicos                  0.000000
kilogramos_producto_Papel, cartón; sus manufacturas           0.000000
kilogramos_producto_Paraguas; sombrillas; bastones           23.163842
kilogramos_producto_Pasta de madera; papel recicl            43.220339
kilogramos_producto_Peletería y sus confecciones             32.485876
kilogramos_producto_Pescados, crustáceos, moluscos            0.000000
kilogramos_producto_Piedra, metal precio; joyer               0.000000
kilogramos_producto_Pieles (exc peleter); cueros              0.282486
kilogramos_producto_Plantas vivas; pro floricultura           0.000000
kilogramos_producto_Plomo y sus manufacturas                 40.395480
kilogramos_producto_Plumas y plumón preparados               32.485876
kilogramos_producto_Prendas de vestir, de punto               0.000000
kilogramos_producto_Prendas de vestir, no de punto            0.000000
kilogramos_producto_Preparac alimenticias diversas            0.000000
kilogramos_producto_Produ fotográfic o cinemato               1.694915
kilogramos_producto_Produc de cereales, de pasteleria         0.282486
kilogramos_producto_Produc de la molinería; malta             8.474576
kilogramos_producto_Product químicos inorgánicos              0.000000
kilogramos_producto_Productos cerámicos                       0.000000
kilogramos_producto_Productos farmacéuticos                   0.000000
kilogramos_producto_Productos químicos orgánicos              0.000000
kilogramos_producto_Pólvoras y explosivos                    60.169492
kilogramos_producto_Relojería                                 0.282486
kilogramos_producto_Residuos industria alimentaria            0.000000
kilogramos_producto_Sal, yeso, piedras s/ trabajar            0.000000
kilogramos_producto_Seda                                     42.090395
kilogramos_producto_Semillas oleagi; plantas industriales     0.847458
kilogramos_producto_Tabaco y sus sucedáneos                  11.864407
kilogramos_producto_Tanino; materias colorantes; pintura      0.000000
kilogramos_producto_Tejidos de punto                          0.000000
kilogramos_producto_Tejidos técnicos, recubiertos             0.000000
kilogramos_producto_Terciopelo; c/ bucles; tapicería          0.000000
kilogramos_producto_Vehículos automóviles; tractores          0.000000
kilogramos_producto_Vehículos, material ferroviarios         20.621469
kilogramos_producto_Vidrio y sus manufacturas                 0.000000
dtype: float64

df = generate_target_values_taric(data, "descripcion_taric", "kilogramos", fillna_flag=True)
df.head(3)

plot_time_series(df, num_columns=3, y_label="Kilogramos", x_label="Fecha")

del data
del df

def get_general_data():
    query = f"""
        SELECT 
            fecha,
            tipo_movimiento_cod,
            cod_pais,
            nombre_pais,
            nivel_taric,
            cod_taric,
            descripcion_taric,
            sum(dolares) as dolares,
            sum(kilogramos) as kilogramos
        FROM `unir-predictiv0-andina-espana.datacomex.comex_comunidad_andina_modelo` 
        WHERE nivel_taric = '1'
        GROUP BY 1,2,3,4,5,6,7
    """

    data = bqh.get_bigquery_data(query)
    data['fecha'] = pd.to_datetime(data['fecha'])
    return data

data = get_general_data()
data.head()

def check_columns(df, required_columns):
    missing_columns = [
        column for column in required_columns if column not in df.columns
    ]
    if missing_columns:
        raise ValueError(
            f"The DataFrame is missing the following columns: {missing_columns}"
        )
    else:
        print("The DataFrame contains all the required columns.")

def generate_target_values(df, target):
    required_columns = ["fecha", "tipo_movimiento_cod", "cod_pais", target]
    check_columns(df, required_columns)

    unique_dates = (
        df[["fecha"]].drop_duplicates().sort_values("fecha").reset_index(drop=True)
    )

    total_target_by_country = (
        df.groupby(["fecha", "cod_pais"])[target]
        .sum()
        .unstack(fill_value=0)
        .add_prefix(f"{target}_T_")
        .reset_index()
    )

    dfs = list()
    for tipo_movimiento in ["I", "E"]:
        data = (
            df[df["tipo_movimiento_cod"] == tipo_movimiento]
            .groupby(["fecha", "cod_pais"])[target]
            .sum()
            .unstack(fill_value=0)
            .add_prefix(f"{target}_{tipo_movimiento}_")
            .reset_index()
        )
        dfs.append(data)

        data = (
            df[df["tipo_movimiento_cod"] == tipo_movimiento]
            .groupby("fecha")[target]
            .sum()
            .rename(f"{target}_{tipo_movimiento}_GLOBAL")
            .reset_index()
        )
        dfs.append(data)

    result_df = unique_dates.merge(total_target_by_country, on="fecha", how="left")

    for df in dfs:
        result_df = result_df.merge(df, on="fecha", how="left")

    numeric_columns = result_df.select_dtypes(include=["number"]).columns
    result_df[numeric_columns] = result_df[numeric_columns].fillna(0)

    return result_df

df = generate_target_values(data, 'dolares')
df.head(3)

The DataFrame contains all the required columns.

plot_time_series(df, num_columns=3, y_label="dolares", x_label="Fecha")

del data
del df

def fit_auto_arima_iterative(train, test):
    best_mape = float("inf")
    best_forecast = None
    best_future = None

    for seasonal in [True, False]:
        if seasonal == True:
            for m in [6, 12, 24]:  # Periodicidades comunes
                try:
                    model = auto_arima(
                        train,
                        m=m,
                        trace=False,
                        error_action="ignore",
                        suppress_warnings=True,
                    )
                    forecast = model.predict(n_periods=len(test))
                    mape = mean_absolute_percentage_error(test, forecast)
                    if mape < best_mape:
                        best_mape = mape
                        best_forecast = forecast
                        best_future = model.predict(n_periods=30)
                except Exception as e:
                    print(f"Error with seasonal={seasonal}, m={m}: {e}")
        else:
            try:
                model = auto_arima(
                    train,
                    seasonal=seasonal,
                    trace=False,
                    error_action="ignore",
                    suppress_warnings=True,
                )
                forecast = model.predict(n_periods=len(test))
                mape = mean_absolute_percentage_error(test, forecast)
                if mape < best_mape:
                    best_mape = mape
                    best_forecast = forecast
                    best_future = model.predict(n_periods=30)
            except Exception as e:
                print(f"Error with seasonal={seasonal}: {e}")

    return best_forecast, best_future, best_mape

def fit_lstm_iterative(train, test, future_steps=6):

    best_mape = float("inf")
    best_forecast = None
    best_future = None

    for units in [50, 100, 150]:
        for epochs in [10, 20]:
            try:
                # Preprocesamiento
                train_reshaped = np.reshape(train, (len(train), 1, 1))
                test_reshaped = np.reshape(test, (len(test), 1, 1))

                # Crear y entrenar el modelo LSTM
                model = Sequential()
                model.add(Input(shape=(1, 1)))
                model.add(LSTM(units, activation="relu"))
                model.add(Dense(1))
                model.compile(optimizer="adam", loss="mean_squared_error")
                model.fit(
                    train_reshaped,
                    train_reshaped[:, 0, 0],
                    epochs=epochs,
                    batch_size=32,
                    verbose=0,
                )

                # Predicción sobre el conjunto de prueba
                forecast = model.predict(test_reshaped).flatten()
                mape = mean_absolute_percentage_error(test, forecast)

                # Actualizar el mejor modelo si el MAPE mejora
                if mape < best_mape:
                    best_mape = mape
                    best_forecast = forecast

                    # Predicción iterativa para el futuro
                    future_input = test_reshaped[
                        -1
                    ]  # Comienza con la última entrada de prueba
                    best_future = []
                    for _ in range(
                        future_steps
                    ):  # Generar las predicciones configuradas
                        future_pred = model.predict(
                            future_input.reshape(1, 1, 1)
                        ).flatten()[0]
                        best_future.append(future_pred)
                        # Actualizar la entrada futura con la predicción actual
                        future_input = np.array([[future_pred]])
            except Exception as e:
                print(f"Error con units={units}, epochs={epochs}: {e}")

    return np.array(best_forecast), np.array(best_future), best_mape

def fit_mlp_iterative(train, test, future_steps=6):
    best_mape = float("inf")
    best_forecast = None
    best_future = None

    for hidden_units in [(64,), (64, 32), (128, 64)]:
        for epochs in [10, 20]:
            try:
                # Preprocesamiento
                train_reshaped = train.reshape(-1, 1)
                test_reshaped = test.reshape(-1, 1)

                # Crear y entrenar el modelo MLP
                model = Sequential()
                model.add(Input(shape=(1,)))
                for units in hidden_units:
                    model.add(Dense(units, activation="relu"))
                model.add(Dense(1))
                model.compile(optimizer="adam", loss="mean_squared_error")
                model.fit(
                    train_reshaped,
                    train_reshaped,
                    epochs=epochs,
                    batch_size=32,
                    verbose=0,
                )

                # Predicción sobre el conjunto de prueba
                forecast = model.predict(test_reshaped).flatten()
                mape = mean_absolute_percentage_error(test, forecast)

                # Actualizar el mejor modelo si el MAPE mejora
                if mape < best_mape:
                    best_mape = mape
                    best_forecast = forecast

                    # Predicción iterativa para el futuro
                    future_input = test_reshaped[
                        -1
                    ]  # Comienza con la última entrada de prueba
                    best_future = []
                    for _ in range(
                        future_steps
                    ):  # Generar las predicciones configuradas
                        future_pred = model.predict(
                            future_input.reshape(1, -1)
                        ).flatten()[0]
                        best_future.append(future_pred)
                        # Actualizar la entrada futura con la predicción actual
                        future_input = np.array([future_pred]).reshape(1, -1)
            except Exception as e:
                print(f"Error con hidden_units={hidden_units}, epochs={epochs}: {e}")

    return np.array(best_forecast), np.array(best_future), best_mape

def plot_results(train, test, forecast, future, title):
    plt.figure(figsize=(10, 5))
    plt.plot(train, label="Entrenamiento", color="blue")
    plt.plot(
        range(len(train), len(train) + len(test)), test, label="Prueba", color="orange"
    )
    plt.plot(
        range(len(train), len(train) + len(forecast)),
        forecast,
        label=f"Predicción ({title})",
        color="green",
    )
    plt.plot(
        range(len(train) + len(test), len(train) + len(test) + len(future)),
        future,
        "--",
        label="Futuro",
        color="red",
    )
    plt.title(f"Resultados del modelo {title}")
    plt.legend()
    plt.show()

def plot_results_side_by_side(
    train,
    test,
    arima_forecast,
    arima_future,
    lstm_forecast,
    lstm_future,
    mlp_forecast,
    mlp_future,
    col,
):
    fig, axes = plt.subplots(1, 3, figsize=(20, 5), sharey=True)
    models = [
        ("ARIMA", arima_forecast, arima_future),
        ("LSTM", lstm_forecast, lstm_future),
        ("MLP", mlp_forecast, mlp_future),
    ]

    for ax, (title, forecast, future) in zip(axes, models):
        ax.plot(train, label="Entrenamiento", color="blue")
        ax.plot(
            range(len(train), len(train) + len(test)),
            test,
            label="Prueba",
            color="orange",
        )
        ax.plot(
            range(len(train), len(train) + len(forecast)),
            forecast,
            label=f"Predicción ({title})",
            color="green",
        )
        ax.plot(
            range(len(train) + len(test), len(train) + len(test) + len(future)),
            future,
            "--",
            label="Futuro",
            color="red",
        )
        ax.set_title(f"Resultados del modelo {title} - {col}")
        ax.legend()

    plt.tight_layout()
    plt.show()

def train_models(df, charts=False, chart_type="side_by_side"):
    results_df = pd.DataFrame()
    columns = df.columns

    for col in tqdm(columns, desc="Procesando serie"):

        if col == "fecha":
            continue

        print(f"Inicia proceso variable: {col}")
    
        series_data = df[col].values

        if not np.issubdtype(series_data.dtype, np.number):
            print(f"Saltando columna {col} porque no es numérica.")
            continue

        train_size = int(len(series_data) * 0.8)
        train, test = series_data[:train_size], series_data[train_size:]

        try:
            arima_forecast, arima_future, arima_mape = fit_auto_arima_iterative(train, test)
            arima_mape = mean_absolute_percentage_error(test, arima_forecast)

            lstm_forecast, lstm_future, lstm_mape = fit_lstm_iterative(
                np.array(train), np.array(test)
            )
            lstm_mape = mean_absolute_percentage_error(test, lstm_forecast)

            mlp_forecast, mlp_future, mlp_mape = fit_mlp_iterative(
                np.array(train), np.array(test)
            )
            mlp_mape = mean_absolute_percentage_error(test, mlp_forecast)

            if charts:
                if chart_type == "side_by_side":
                    plot_results_side_by_side(
                        train,
                        test,
                        arima_forecast,
                        arima_future,
                        lstm_forecast,
                        lstm_future,
                        mlp_forecast,
                        mlp_future,
                        col,
                    )
                else:
                    plot_results(train, test, arima_forecast, arima_future, f"ARIMA - {col}")
                    plot_results(train, test, lstm_forecast, lstm_future, f"LSTM - {col}")
                    plot_results(train, test, mlp_forecast, mlp_future, f"MLP - {col}")

            forecast_df = pd.DataFrame(
                {
                    "FORECAST_LEVEL": [
                        "MAPE",
                        "Forecast_1",
                        "Forecast_2",
                        "Forecast_3",
                        "Forecast_4",
                        "Forecast_5",
                        "Forecast_6",
                    ],
                    "ARIMA": [arima_mape] + arima_future[:6].tolist(),
                    "LSTM": [lstm_mape] + lstm_future[:6].tolist(),
                    "MLP": [mlp_mape] + mlp_future[:6].tolist(),
                }
            )
            forecast_df["Producto"] = col
            results_df = pd.concat([results_df, forecast_df], ignore_index=True)
        except Exception as e:
            print(f"Error al procesar {col}: {e}")
        print(f"Finaliza proceso variable: {col}")

    return results_df

def restructure_result(results):
    results[['llave1', 'llave2', 'llave3']] = results['Producto'].str.split('_', expand=True).fillna('N/A')
    results = pd.melt(
        results,
        id_vars=['FORECAST_LEVEL', 'Producto', 'llave1','llave2', 'llave3'],
        value_vars=['ARIMA', 'LSTM', 'MLP'],
        var_name='modelo',
        value_name='prediccion'
    )
    results['fecha_prediccion'] = pd.Timestamp.now()

    mape = results[results['FORECAST_LEVEL'] == 'MAPE']

    forecast_dates = {
        "Forecast_1": "2024-07-01",
        "Forecast_2": "2024-08-01",
        "Forecast_3": "2024-09-01",
        "Forecast_4": "2024-10-01",
        "Forecast_5": "2024-11-01",
        "Forecast_6": "2024-12-01",
    }
    results['fecha'] = pd.to_datetime(results['FORECAST_LEVEL'].map(forecast_dates))
    forecast = results[results['FORECAST_LEVEL'] != 'MAPE']
    return forecast, mape

data = get_general_data()

for target in ['dolares', 'kilogramos']:
    print(f"Procesando target: {target}")
    df = generate_target_values(data, target)
    results = train_models(df, charts=False)
    forecast, mape = restructure_result(results)

    bqh.create_or_replace_table(
        dataset_id = 'datacomex',
        table_id = 'comex_comunidad_andina_forecast',
        dataframe = forecast,
        write_disposition="WRITE_APPEND"
    )

    bqh.create_or_replace_table(
        dataset_id = 'datacomex',
        table_id = 'comex_comunidad_andina_mape',
        dataframe = mape,
        write_disposition="WRITE_APPEND"
    )

Procesando target: dolares
The DataFrame contains all the required columns.

Procesando serie:   0%|          | 0/15 [00:00<?, ?it/s]

Inicia proceso variable: dolares_T_480
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 43ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 56ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step

Procesando serie:  13%|█▎        | 2/15 [01:01<06:38, 30.67s/it]

Finaliza proceso variable: dolares_T_480
Inicia proceso variable: dolares_T_500
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 43ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 43ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step

Procesando serie:  20%|██        | 3/15 [01:48<07:28, 37.41s/it]

Finaliza proceso variable: dolares_T_500
Inicia proceso variable: dolares_T_504
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 52ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step

Procesando serie:  27%|██▋       | 4/15 [02:44<08:10, 44.59s/it]

Finaliza proceso variable: dolares_T_504
Inicia proceso variable: dolares_T_516
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 26ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 52ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step

Procesando serie:  33%|███▎      | 5/15 [04:15<10:07, 60.70s/it]

target = 'dolares'
num_sets = 5
df = get_taric_data()
df = generate_target_values_taric(data, "cod_taric", target, fillna_flag=True)
df_num_columns = len(df.columns)

for i in range(0, df_num_columns, num_sets):
    print(f"Procesando: {i+num_sets} de {df_num_columns} series")
    try:
        subset_columns = df.columns[i:i+num_sets]
        subset_df = df[['fecha'] + list(subset_columns)]
        results = train_models(subset_df, charts=False)
        forecast, mape = restructure_result(results)

        bqh.create_or_replace_table(
            dataset_id = 'datacomex',
            table_id = 'comex_comunidad_andina_forecast',
            dataframe = forecast,
            write_disposition="WRITE_APPEND"
        )

        bqh.create_or_replace_table(
            dataset_id = 'datacomex',
            table_id = 'comex_comunidad_andina_mape',
            dataframe = mape,
            write_disposition="WRITE_APPEND"
        )

    except Exception as e: 
        print(f"Error: {e}")
        print(f"Error Procesando columnas: {df.columns[i:i+num_sets]}")

Procesando: 5 de 99 series

Procesando serie:   0%|          | 0/6 [00:00<?, ?it/s]

Inicia proceso variable: dolares_producto_01
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step
1/3 ━━━━━━━━━━━━━━━━━━━━ 0s 70ms/stepWARNING:tensorflow:5 out of the last 13 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x000001F224D09B20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step
WARNING:tensorflow:6 out of the last 14 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x000001F1767D89A0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step

Procesando serie:  50%|█████     | 3/6 [02:48<02:48, 56.11s/it]

Finaliza proceso variable: dolares_producto_01
Inicia proceso variable: dolares_producto_02
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

Procesando serie:  67%|██████▋   | 4/6 [03:58<02:01, 60.60s/it]

Finaliza proceso variable: dolares_producto_02
Inicia proceso variable: dolares_producto_03
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step

Procesando serie:  83%|████████▎ | 5/6 [06:59<01:38, 98.62s/it]

Finaliza proceso variable: dolares_producto_03
Inicia proceso variable: dolares_producto_04
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step

Procesando serie: 100%|██████████| 6/6 [08:19<00:00, 83.23s/it]

target = 'kilogramos'
num_sets = 5
df = get_taric_data()
df = generate_target_values_taric(data, "cod_taric", target, fillna_flag=True)
df_num_columns = len(df.columns)

for i in range(0, df_num_columns, num_sets):
    print(f"Procesando: {i+num_sets} de {df_num_columns} columnas")
    try:
        subset_columns = df.columns[i:i+num_sets]
        subset_df = df[['fecha'] + list(subset_columns)]
        results = train_models(subset_df, charts=False)
        forecast, mape = restructure_result(results)

        bqh.create_or_replace_table(
            dataset_id = 'datacomex',
            table_id = 'comex_comunidad_andina_forecast',
            dataframe = forecast,
            write_disposition="WRITE_APPEND"
        )

        bqh.create_or_replace_table(
            dataset_id = 'datacomex',
            table_id = 'comex_comunidad_andina_mape',
            dataframe = mape,
            write_disposition="WRITE_APPEND"
        )

    except Exception as e: 
        print(f"Error: {e}")
        print(f"Error Procesando columnas: {df.columns[i:i+num_sets]}")

Procesando: 5 de 99 columnas

Procesando serie:   0%|          | 0/6 [00:00<?, ?it/s]

Inicia proceso variable: kilogramos_producto_01
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step

Procesando serie:  50%|█████     | 3/6 [01:20<01:20, 26.81s/it]

Finaliza proceso variable: kilogramos_producto_01
Inicia proceso variable: kilogramos_producto_02
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 52ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step

Procesando serie:  67%|██████▋   | 4/6 [01:52<00:57, 28.65s/it]

Finaliza proceso variable: kilogramos_producto_02
Inicia proceso variable: kilogramos_producto_03
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step

Procesando serie:  83%|████████▎ | 5/6 [03:39<00:53, 53.10s/it]

Finaliza proceso variable: kilogramos_producto_03
Inicia proceso variable: kilogramos_producto_04
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 43ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step
3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step

Procesando serie: 100%|██████████| 6/6 [05:02<00:00, 50.44s/it]

	fecha	anio	mes	tipo_movimiento_cod	tipo_movimiento	estado_transaccion	cod_pais	nombre_pais	cod_provincia	nombre_provincia	cod_comunidad	nombre_comunidad	nivel_taric	cod_taric	descripcion_taric	euros	dolares	kilogramos
0	2005-10-01	2005	10	I	Importacion	definitivo	480	Colombia	27	Lugo	10	Galicia	3	620349	De las demás materias textiles	358.53	430.77	8.0
1	2005-10-01	2005	10	I	Importacion	definitivo	500	Ecuador	24	León	06	Castilla y León	4	61101130	Suéteres "jerseys" (exc suéteres >= 50% )	1875.44	2253.34	371.0
2	2005-10-01	2005	10	E	Exportacion	definitivo	504	Perú	24	León	06	Castilla y León	3	930621	Cartuchos, escopet /rifle c/ cañón ánima lisa	38700.98	46499.23	18000.0
3	2005-10-01	2005	10	E	Exportacion	definitivo	500	Ecuador	24	León	06	Castilla y León	4	49029090	Diarios y publicaciones periódicas, impresos	322.08	386.98	90.0
4	2005-10-01	2005	10	E	Exportacion	definitivo	500	Ecuador	42	Soria	06	Castilla y León	3	130219	Los demás	27822.32	33428.52	1845.0

	fecha	cod_taric	descripcion_taric	dolares	kilogramos
0	2015-08-01	75	Níquel y sus manufacturas	1020.72	48.0
1	2008-08-01	75	Níquel y sus manufacturas	3672.21	4.0
2	2022-03-01	36	Pólvoras y explosivos	1234442.52	383369.2
3	2022-03-01	75	Níquel y sus manufacturas	51003.10	1328.0
4	2023-08-01	36	Pólvoras y explosivos	887540.36	98520.0

	fecha	kilogramos_producto_Abonos	kilogramos_producto_Aceites esenciales; perfumer	kilogramos_producto_Aeronaves; vehículos espaciales	kilogramos_producto_Alfombras; revesti textiles suelo	kilogramos_producto_Algodón	kilogramos_producto_Aluminio y sus manufacturas	kilogramos_producto_Animales vivos	kilogramos_producto_Aparatos y material eléctricos	kilogramos_producto_Aparatos ópticos, medida, médicos	kilogramos_producto_Armas y municiones; accesorios	kilogramos_producto_Artíc textilhogar; prendería	kilogramos_producto_Artículos de sombrerería	kilogramos_producto_Azúcares; artículos confitería	kilogramos_producto_Barcos y embarcaciones	kilogramos_producto_Bebidas todo tipo (exc zumos)	kilogramos_producto_Cacao y sus preparaciones	kilogramos_producto_Café, té, yerba mate y especias	kilogramos_producto_Calzado; sus partes	kilogramos_producto_Carne y despojos comestibles	kilogramos_producto_Caucho y sus manufacturas	kilogramos_producto_Cereales	kilogramos_producto_Cinc y sus manufacturas	kilogramos_producto_Cobre y sus manufacturas	kilogramos_producto_Combustibles, aceites mineral	kilogramos_producto_Conjunt de otros productos	kilogramos_producto_Conjunt indust; regl 840/96	kilogramos_producto_Conservas de carne o pescado	kilogramos_producto_Conservas verdura o fruta; zumo	kilogramos_producto_Corcho y sus manufacturas	kilogramos_producto_Estaño y sus manufacturas	kilogramos_producto_Fibras sinté, artif discont	kilogramos_producto_Fieltro, tela s/ tejer; cordelería	kilogramos_producto_Filamentos sintéticos o artificiales	kilogramos_producto_Frutas /frutos, s/ conservar	kilogramos_producto_Fundición, hierro y acero	kilogramos_producto_Grasas, aceite animal o vegetal	kilogramos_producto_Herramien y cuchill metálic	kilogramos_producto_Instrumentos musicales	kilogramos_producto_Jabones; lubricantes; ceras	kilogramos_producto_Jugos y extractos vegetales	kilogramos_producto_Juguetes, juegos, artíc deportivos	kilogramos_producto_Lana y pelo fino	kilogramos_producto_Leche, productos lácteos; huevos	kilogramos_producto_Legumbres, hortalizas, s/ conserv	kilogramos_producto_Libros, publicaci; artes gráf	kilogramos_producto_Madera y sus manufacturas	kilogramos_producto_Manuf de fundic, hier/acero	kilogramos_producto_Manufact de cuero, marroquinería	kilogramos_producto_Manufact diversas de metales	kilogramos_producto_Manufacturas de cestería	kilogramos_producto_Manufacturas de piedra, yeso	kilogramos_producto_Manufacturas diversas	kilogramos_producto_Mat plásticas; sus manufactu	kilogramos_producto_Materias albuminóideas; colas	kilogramos_producto_Materias trenzables	kilogramos_producto_Minerales, escorias y cenizas	kilogramos_producto_Muebles, sillas, lámparas	kilogramos_producto_Máquinas y aparatos mecánicos	kilogramos_producto_Níquel y sus manufacturas	kilogramos_producto_Objetos de arte; antiguedades	kilogramos_producto_Otras fibras textiles vegetales	kilogramos_producto_Otros metal comunes; cermets	kilogramos_producto_Otros productos de origen animal	kilogramos_producto_Otros productos químicos	kilogramos_producto_Papel, cartón; sus manufacturas	kilogramos_producto_Paraguas; sombrillas; bastones	kilogramos_producto_Pasta de madera; papel recicl	kilogramos_producto_Peletería y sus confecciones	kilogramos_producto_Pescados, crustáceos, moluscos	kilogramos_producto_Piedra, metal precio; joyer	kilogramos_producto_Pieles (exc peleter); cueros	kilogramos_producto_Plantas vivas; pro floricultura	kilogramos_producto_Plomo y sus manufacturas	kilogramos_producto_Plumas y plumón preparados	kilogramos_producto_Prendas de vestir, de punto	kilogramos_producto_Prendas de vestir, no de punto	kilogramos_producto_Preparac alimenticias diversas	kilogramos_producto_Produ fotográfic o cinemato	kilogramos_producto_Produc de cereales, de pasteleria	kilogramos_producto_Produc de la molinería; malta	kilogramos_producto_Product químicos inorgánicos	kilogramos_producto_Productos cerámicos	kilogramos_producto_Productos farmacéuticos	kilogramos_producto_Productos químicos orgánicos	kilogramos_producto_Pólvoras y explosivos	kilogramos_producto_Relojería	kilogramos_producto_Residuos industria alimentaria	kilogramos_producto_Sal, yeso, piedras s/ trabajar	kilogramos_producto_Seda	kilogramos_producto_Semillas oleagi; plantas industriales	kilogramos_producto_Tabaco y sus sucedáneos	kilogramos_producto_Tanino; materias colorantes; pintura	kilogramos_producto_Tejidos de punto	kilogramos_producto_Tejidos técnicos, recubiertos	kilogramos_producto_Terciopelo; c/ bucles; tapicería	kilogramos_producto_Vehículos automóviles; tractores	kilogramos_producto_Vehículos, material ferroviarios	kilogramos_producto_Vidrio y sus manufacturas
0	1995-01-01	NaN	9666.0	NaN	221.0	34065.0	217.0	310.0	696942.40	8839.5	10910.0	15308.0	58.0	68220.0	NaN	85821.0	88752.0	2478389.0	6434.0	471.45	105833.0	342250.0	NaN	283748.0	3060822.0	94135.9	NaN	898096.00	1519967.0	579.0	183487.0	3690.0	5335.0	31463.0	5534772.0	821398.0	159470.0	59589.0	37.0	57571.0	1362.0	28023.0	5112.0	656.00	328846.0	354880.67	53000.0	697642.00	563.0	21294.5	28.0	597059.0	4654.0	418159.0	1582.0	18474.0	5776650.0	29572.0	2000425.70	NaN	2887.0	157359.0	47.0	4030.0	344831.0	641237.0	NaN	NaN	NaN	1114102.0	265.57	128679.5	97984.0	NaN	NaN	33402.00	4077.00	73305.0	72.0	23010.0	21906.0	1332808.0	3749865.5	43627.0	45035.00	NaN	356.0	133400.0	187183.0	NaN	54088.0	378.0	474592.0	2480.0	7314.2	43190.0	164070.0	NaN	518541.0
1	1995-02-01	11193.0	23268.0	13.60	56.0	43800.0	641.0	921.0	322913.92	40580.4	20064.0	11663.0	247.0	52433.0	NaN	56557.5	330639.0	2547193.0	5970.0	189.00	240427.0	161000.0	NaN	1179.0	129518020.0	685383.0	NaN	939627.00	973731.0	5043.0	121765.0	35221.0	495.0	61546.0	6674899.0	565598.0	52082.0	95823.0	245.0	50020.0	2035.0	2153.0	5090.5	17266.02	198077.0	445549.97	22256738.0	568064.00	2127.0	33439.0	323.0	693329.0	6190.0	846890.0	1330.0	2000.0	4946600.0	47760.0	1068732.60	NaN	358.0	135000.0	NaN	7111.0	143445.0	645350.0	NaN	NaN	145.0	5203658.0	1623.93	41349.0	115652.0	NaN	NaN	78101.34	4491.00	76561.0	5.0	12690.0	20160.0	140625.0	4119116.0	13457.0	138453.66	492.0	198.0	200015.0	229065.0	NaN	NaN	233.0	533855.0	6430.0	15859.0	34542.0	186672.0	63460.0	621008.0
2	1995-03-01	41500.0	78788.2	4.36	583.0	69979.0	3059.0	157.0	1931986.40	60836.8	302.0	37013.9	3278.0	86915.0	2500.0	75461.0	673403.0	2455497.0	32352.0	1191.00	318559.2	55000.0	NaN	1598976.0	1182120.0	217493.5	NaN	573687.64	1135688.0	1646.0	147959.0	32758.0	922.0	68532.0	6128509.0	726221.0	239412.0	92013.0	744.0	143865.0	11120.0	3465.0	14455.0	16250.00	238020.0	607892.00	109191.0	561113.38	2414.0	53459.0	9.0	720715.0	7915.0	618511.0	2789.0	18333.0	7944880.0	64686.0	1808349.65	NaN	634.9	184855.0	NaN	24493.0	363874.0	1033167.5	13.0	NaN	NaN	2615742.0	2009.84	60697.0	74346.0	NaN	NaN	86237.45	22133.15	48496.0	35.0	53770.0	20160.0	1890928.0	4264214.0	11171.0	94210.97	NaN	667.0	4752832.0	227573.0	100.0	80078.0	NaN	437818.0	1086.0	8909.0	37884.0	414380.0	9711.0	548670.6

	fecha	kilogramos_producto_Abonos	kilogramos_producto_Aceites esenciales; perfumer	kilogramos_producto_Aeronaves; vehículos espaciales	kilogramos_producto_Alfombras; revesti textiles suelo	kilogramos_producto_Algodón	kilogramos_producto_Aluminio y sus manufacturas	kilogramos_producto_Animales vivos	kilogramos_producto_Aparatos y material eléctricos	kilogramos_producto_Aparatos ópticos, medida, médicos	kilogramos_producto_Armas y municiones; accesorios	kilogramos_producto_Artíc textilhogar; prendería	kilogramos_producto_Artículos de sombrerería	kilogramos_producto_Azúcares; artículos confitería	kilogramos_producto_Barcos y embarcaciones	kilogramos_producto_Bebidas todo tipo (exc zumos)	kilogramos_producto_Cacao y sus preparaciones	kilogramos_producto_Café, té, yerba mate y especias	kilogramos_producto_Calzado; sus partes	kilogramos_producto_Carne y despojos comestibles	kilogramos_producto_Caucho y sus manufacturas	kilogramos_producto_Cereales	kilogramos_producto_Cobre y sus manufacturas	kilogramos_producto_Combustibles, aceites mineral	kilogramos_producto_Conjunt de otros productos	kilogramos_producto_Conservas de carne o pescado	kilogramos_producto_Conservas verdura o fruta; zumo	kilogramos_producto_Corcho y sus manufacturas	kilogramos_producto_Estaño y sus manufacturas	kilogramos_producto_Fibras sinté, artif discont	kilogramos_producto_Fieltro, tela s/ tejer; cordelería	kilogramos_producto_Filamentos sintéticos o artificiales	kilogramos_producto_Frutas /frutos, s/ conservar	kilogramos_producto_Fundición, hierro y acero	kilogramos_producto_Grasas, aceite animal o vegetal	kilogramos_producto_Herramien y cuchill metálic	kilogramos_producto_Instrumentos musicales	kilogramos_producto_Jabones; lubricantes; ceras	kilogramos_producto_Jugos y extractos vegetales	kilogramos_producto_Juguetes, juegos, artíc deportivos	kilogramos_producto_Lana y pelo fino	kilogramos_producto_Leche, productos lácteos; huevos	kilogramos_producto_Legumbres, hortalizas, s/ conserv	kilogramos_producto_Libros, publicaci; artes gráf	kilogramos_producto_Madera y sus manufacturas	kilogramos_producto_Manuf de fundic, hier/acero	kilogramos_producto_Manufact de cuero, marroquinería	kilogramos_producto_Manufact diversas de metales	kilogramos_producto_Manufacturas de cestería	kilogramos_producto_Manufacturas de piedra, yeso	kilogramos_producto_Manufacturas diversas	kilogramos_producto_Mat plásticas; sus manufactu	kilogramos_producto_Materias albuminóideas; colas	kilogramos_producto_Materias trenzables	kilogramos_producto_Minerales, escorias y cenizas	kilogramos_producto_Muebles, sillas, lámparas	kilogramos_producto_Máquinas y aparatos mecánicos	kilogramos_producto_Objetos de arte; antiguedades	kilogramos_producto_Otras fibras textiles vegetales	kilogramos_producto_Otros metal comunes; cermets	kilogramos_producto_Otros productos de origen animal	kilogramos_producto_Otros productos químicos	kilogramos_producto_Papel, cartón; sus manufacturas	kilogramos_producto_Paraguas; sombrillas; bastones	kilogramos_producto_Peletería y sus confecciones	kilogramos_producto_Pescados, crustáceos, moluscos	kilogramos_producto_Piedra, metal precio; joyer	kilogramos_producto_Pieles (exc peleter); cueros	kilogramos_producto_Plantas vivas; pro floricultura	kilogramos_producto_Prendas de vestir, de punto	kilogramos_producto_Prendas de vestir, no de punto	kilogramos_producto_Preparac alimenticias diversas	kilogramos_producto_Produ fotográfic o cinemato	kilogramos_producto_Produc de cereales, de pasteleria	kilogramos_producto_Produc de la molinería; malta	kilogramos_producto_Product químicos inorgánicos	kilogramos_producto_Productos cerámicos	kilogramos_producto_Productos farmacéuticos	kilogramos_producto_Productos químicos orgánicos	kilogramos_producto_Pólvoras y explosivos	kilogramos_producto_Relojería	kilogramos_producto_Residuos industria alimentaria	kilogramos_producto_Sal, yeso, piedras s/ trabajar	kilogramos_producto_Seda	kilogramos_producto_Semillas oleagi; plantas industriales	kilogramos_producto_Tabaco y sus sucedáneos	kilogramos_producto_Tanino; materias colorantes; pintura	kilogramos_producto_Tejidos de punto	kilogramos_producto_Tejidos técnicos, recubiertos	kilogramos_producto_Terciopelo; c/ bucles; tapicería	kilogramos_producto_Vehículos automóviles; tractores	kilogramos_producto_Vehículos, material ferroviarios	kilogramos_producto_Vidrio y sus manufacturas
0	1995-01-01	0.0	9666.0	0.00	221.0	34065.0	217.0	310.0	696942.40	8839.5	10910.0	15308.0	58.0	68220.0	0.0	85821.0	88752.0	2478389.0	6434.0	471.45	105833.0	342250.0	283748.0	3060822.0	94135.9	898096.00	1519967.0	579.0	183487.0	3690.0	5335.0	31463.0	5534772.0	821398.0	159470.0	59589.0	37.0	57571.0	1362.0	28023.0	5112.0	656.00	328846.0	354880.67	53000.0	697642.00	563.0	21294.5	28.0	597059.0	4654.0	418159.0	1582.0	18474.0	5776650.0	29572.0	2000425.70	2887.0	157359.0	47.0	4030.0	344831.0	641237.0	0.0	0.0	1114102.0	265.57	128679.5	97984.0	33402.00	4077.00	73305.0	72.0	23010.0	21906.0	1332808.0	3749865.5	43627.0	45035.00	0.0	356.0	133400.0	187183.0	0.0	54088.0	378.0	474592.0	2480.0	7314.2	43190.0	164070.0	0.0	518541.0
1	1995-02-01	11193.0	23268.0	13.60	56.0	43800.0	641.0	921.0	322913.92	40580.4	20064.0	11663.0	247.0	52433.0	0.0	56557.5	330639.0	2547193.0	5970.0	189.00	240427.0	161000.0	1179.0	129518020.0	685383.0	939627.00	973731.0	5043.0	121765.0	35221.0	495.0	61546.0	6674899.0	565598.0	52082.0	95823.0	245.0	50020.0	2035.0	2153.0	5090.5	17266.02	198077.0	445549.97	22256738.0	568064.00	2127.0	33439.0	323.0	693329.0	6190.0	846890.0	1330.0	2000.0	4946600.0	47760.0	1068732.60	358.0	135000.0	0.0	7111.0	143445.0	645350.0	0.0	145.0	5203658.0	1623.93	41349.0	115652.0	78101.34	4491.00	76561.0	5.0	12690.0	20160.0	140625.0	4119116.0	13457.0	138453.66	492.0	198.0	200015.0	229065.0	0.0	0.0	233.0	533855.0	6430.0	15859.0	34542.0	186672.0	63460.0	621008.0
2	1995-03-01	41500.0	78788.2	4.36	583.0	69979.0	3059.0	157.0	1931986.40	60836.8	302.0	37013.9	3278.0	86915.0	2500.0	75461.0	673403.0	2455497.0	32352.0	1191.00	318559.2	55000.0	1598976.0	1182120.0	217493.5	573687.64	1135688.0	1646.0	147959.0	32758.0	922.0	68532.0	6128509.0	726221.0	239412.0	92013.0	744.0	143865.0	11120.0	3465.0	14455.0	16250.00	238020.0	607892.00	109191.0	561113.38	2414.0	53459.0	9.0	720715.0	7915.0	618511.0	2789.0	18333.0	7944880.0	64686.0	1808349.65	634.9	184855.0	0.0	24493.0	363874.0	1033167.5	13.0	0.0	2615742.0	2009.84	60697.0	74346.0	86237.45	22133.15	48496.0	35.0	53770.0	20160.0	1890928.0	4264214.0	11171.0	94210.97	0.0	667.0	4752832.0	227573.0	100.0	80078.0	0.0	437818.0	1086.0	8909.0	37884.0	414380.0	9711.0	548670.6

	fecha	dolares_T_480	dolares_T_500	dolares_T_504	dolares_T_516	dolares_I_480	dolares_I_500	dolares_I_504	dolares_I_516	dolares_I_GLOBAL	dolares_E_480	dolares_E_500	dolares_E_504	dolares_E_516	dolares_E_GLOBAL
0	1995-01-01	30196482.48	13092733.24	20857372.05	7715333.95	13864052.77	8500822.24	9542087.75	1214969.00	33121931.76	16332429.71	4591911.00	11315284.3	6500364.95	38739989.96
1	1995-02-01	39119266.62	22325139.33	22881736.74	2425193.36	20009694.94	15862975.37	7030612.74	826383.74	43729666.79	19109571.68	6462163.96	15851124.0	1598809.62	43021669.26
2	1995-03-01	39962424.96	27015607.59	45449699.04	2394522.82	13100617.29	17719781.20	15869715.94	925948.37	47616062.80	26861807.67	9295826.39	29579983.1	1468574.45	67206191.61

Universidad Internacional de La Rioja¶

Máster Universitario en Visual Analytics and Big Data¶

Predicción y Análisis de la Demanda y Suministro de Productos entre la Comunidad Andina y España¶

Notebook 5: Modelado - Proceso General¶

Librerias¶

Configuracion¶

Validacion estructura de datos¶

Estructuracion datos por Sector - Nivel taric 1¶

Estructuracion de los datos Generales¶

Definicion de modelos¶

Entrenamiento nivel General¶

Entrenamiento por productos - Dolares¶

Entrenamiento por productos - Kilogramos¶

	fecha	tipo_movimiento_cod	cod_pais	nombre_pais	nivel_taric	cod_taric	descripcion_taric	dolares	kilogramos
0	2014-03-01	E	480	Colombia	1	45	Corcho y sus manufacturas	5974.07	317.0
1	2017-09-01	E	500	Ecuador	1	66	Paraguas; sombrillas; bastones	5053.26	204.0
2	2017-09-01	I	500	Ecuador	1	14	Materias trenzables	32212.35	17232.0
3	2007-03-01	E	500	Ecuador	1	89	Barcos y embarcaciones	162691.11	2170.0
4	2003-09-01	E	500	Ecuador	1	79	Cinc y sus manufacturas	4661.99	1093.0